taxonomy classfier : taxnomic weights

Hi :slight_smile:

Sorry to bother you. I have a few questions related to taxonomy classifier.

I am reading your current protocol paper for the static version 2019.10.
And in the tutorial part, When you train the classifier, you mentioned a taxonomic weights file: pre-calculated taxonomic weights specific to human stool data. But in the online tutorial version, it doesn’t mention this procedure.

And also in the paper, you mentioned it is optional. In my case, I have samples from shell or sand. It’s kind of different source. In that case, I couldn’t find an exactly matched pre-calculated one in So I assume it is okay to skip this file?

I have noticed that all cases are tested by gg13_8, but not the SLIVA, which updates more frequently. Is that because the gg13_8 is human-curated and more precise?

And in the paper, you also used the 99% but not the 97%, is it better to use 99%?

Thanks so much!


Hi @Lu_Zhang,

It’s not really clear what you are referring to here. If this is important for answering your questions, could you please link to the source?

You could follow this tutorial if you would like to train your own:

Or determine the most appropriate EMPO-3 type from the earth microbiome project (sand would fit soil or sediment, but not sure about shell, maybe animal surface?).

I am not really sure what you are referring to here. As you can see in the readytowear repository you linked to, there are pre-assembled taxonomic weights compatible with SILVA, greengenes, and GTDB taxonomies. Taxonomic weights can be assembled based on any taxonomy of your choosing, following the tutorial above.

Again it is not clear what paper you are referring to here, but I recommend searching this forum for a detailed answer to this question. Yes, in general it is better to use more specific taxonomic databases, and a database clustered at 97% similarity will lose some resolution compared to 99%.

I hope that helps!

sorry I forget to mention the paper name : QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data


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